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Define period for vector to learn in first signal
period_start_1 <- "2019-02-12 03:38:00" # YYYY-MM-DD hh:mm:ss
period_end_1 <- "2019-02-12 03:46:00" # YYYY-MM-DD hh:mm:ss
Define period from second signal where to find learned signal
period_start_2 <- "2019-02-24 12:00:00" # YYYY-MM-DD hh:mm:ss
period_end_2 <- "2019-02-24 20:00:00" # YYYY-MM-DD hh:mm:ss
step_size = 4 # step size for discretization in seconds
Signal length - Data points number in signal
Time mode - Mode of time change frequency
Value mode - Most frequent value
Sd - Standard deviation
Mean - Average value
n5, n25, n75, n95 - Quantile values
Kurtosis - Kurtosis quantifies the peak value of the PDF (positive kurtosis tells that there is lot of data in tails, negative - little data in tails
Skewness - Skewness quantifies the asymmetry behavior of vibration signal through its PDF
RMS - Root mean square value changes faster then mean
Entropy - Amount of uncertainty in an entire probability distribution
Crest factor (cf) - Ratio of the instantaneous peak amplitude of a waveform, to its root mean square RMS value
Shape factor (sf) - Shape factor is a value that is affected by an object’s shape but is independent of its dimensions
Mean crossing - Mean value crossing count
Data was discretized every 4 seconds
Generalized additive model formula:
In our case formula is :
\(g(E(Y)) = \beta_{0} + f_{1}(x_{1}) + \varepsilon\),
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